{"id":"W3088503605","doi":"10.1007/s11761-020-00301-1","title":"Semantics-based API discovery, matching and composition with linked metadata","year":2020,"lang":"en","type":"article","venue":"Service Oriented Computing and Applications","topic":"Service-Oriented Architecture and Web Services","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Athabasca University; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; USable; SPARQL; Semantic Web; Semantics (computer science); Web service; Programming language; Database; Information retrieval; World Wide Web; RDF","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001114641,0.0002466509,0.0002397907,0.00007911846,0.0005452757,0.0005711598,0.0005600332,0.00005642849,8.735941e-7],"category_scores_gemma":[0.000001348219,0.0002106237,0.00002794093,0.001040594,0.00004323397,0.0006087028,0.000434448,0.0002363923,0.000006942978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009996088,"about_ca_system_score_gemma":0.00004771514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001899412,"about_ca_topic_score_gemma":0.00007208385,"domain_scores_codex":[0.9984222,0.00006055381,0.0002597797,0.000725765,0.0002471699,0.000284539],"domain_scores_gemma":[0.9988199,0.0001548183,0.0001625657,0.0004964449,0.000140527,0.0002256903],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002160505,0.0006448204,0.005517145,0.002393807,0.0004324333,0.00003318942,0.02334662,0.02814989,0.02591696,0.8611838,0.00006189055,0.05210341],"study_design_scores_gemma":[0.002662132,0.0003449927,0.004743536,0.0003983218,0.0002180359,0.00008530992,0.001775048,0.9632893,0.003846241,0.004420556,0.01708991,0.001126606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1674883,0.0001533181,0.8172922,0.01420467,0.00002879908,0.0003612492,0.00001654108,0.0003269379,0.0001280179],"genre_scores_gemma":[0.9251153,0.000009858254,0.0640295,0.01054034,0.0001411369,0.00002758651,0.0001126066,0.00001975865,0.000003927744],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9351394,"threshold_uncertainty_score":0.8588986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009496095831223753,"score_gpt":0.2291688873851305,"score_spread":0.2196727915539067,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}